Detailed balance in large language model-driven agents
4 days ago
- #Generative Dynamics
- #AI Theory
- #Large Language Models
- LLM-driven agents are emerging as a powerful paradigm for solving complex problems.
- A theoretical framework to understand their macroscopic dynamics is currently lacking.
- The study proposes a method based on the least action principle to estimate generative directionality in LLMs.
- Experimental measurements reveal a detailed balance in LLM-generated state transitions.
- LLM generation may involve learning underlying potential functions rather than rule sets.
- This discovery represents the first macroscopic physical law in LLM generative dynamics.
- The work aims to elevate AI agent studies from engineering practices to a quantifiable science.